Thesis Abstract

Abstract
Landslides pose a significant threat to communities and infrastructure in various regions around the world. Understanding the factors that contribute to landslide susceptibility is crucial for effective risk assessment and mitigation strategies. This study focuses on analyzing landslide susceptibility in a specific region using Geographic Information System (GIS) and remote sensing techniques. The research aims to identify the key factors influencing landslide occurrence and to develop a predictive model for assessing landslide susceptibility. The thesis begins with an introduction that provides an overview of the research topic and its importance. The background of the study highlights the existing knowledge gaps in landslide susceptibility assessment and the need for advanced techniques like GIS and remote sensing. The problem statement emphasizes the challenges posed by landslides and the significance of developing proactive measures to mitigate their impact. The objectives of the study outline the specific goals that the research aims to achieve, including identifying landslide-prone areas and assessing the spatial distribution of susceptibility factors. The limitations of the study are also discussed to acknowledge any constraints that may affect the research outcomes. The scope of the study defines the geographical area under investigation and the specific methodologies employed. The significance of the study emphasizes the potential impact of the research findings on land-use planning, disaster management, and infrastructure development. The structure of the thesis provides a roadmap of the chapters and their contents, guiding the reader through the research process. Chapter two presents a comprehensive literature review that synthesizes existing studies on landslide susceptibility assessment, GIS applications, and remote sensing techniques. The review highlights the current methodologies, challenges, and advancements in the field, laying the foundation for the research methodology. Chapter three details the research methodology, including data collection, preprocessing, and analysis techniques. The chapter outlines the steps involved in GIS-based spatial analysis and remote sensing data interpretation. The selection of landslide susceptibility factors and the development of a susceptibility model are described in detail, providing insights into the research process. Chapter four presents a thorough discussion of the research findings, including the spatial distribution of landslide susceptibility in the study area and the validation of the susceptibility model. The chapter analyzes the factors contributing to landslide occurrence and assesses the accuracy of the predictive model. Chapter five concludes the thesis by summarizing the key findings, discussing their implications, and highlighting the contributions of the research. The conclusions drawn from the study inform recommendations for future research directions and practical applications in landslide risk management. In conclusion, this thesis contributes to the field of landslide susceptibility assessment by integrating GIS and remote sensing techniques to analyze landslide-prone areas in a specific region. The research findings provide valuable insights for decision-makers, planners, and researchers working towards mitigating the impact of landslides and enhancing community resilience in vulnerable areas.

Thesis Overview

The project titled "Analysis of landslide susceptibility in a specific region using GIS and remote sensing techniques" aims to investigate the factors influencing landslide occurrences in a particular area through the application of Geographic Information System (GIS) and remote sensing technologies. Landslides are a significant geological hazard that poses threats to human lives, infrastructure, and the environment, making it crucial to understand and assess their susceptibility in specific regions. The research will begin with a comprehensive literature review to explore existing studies on landslide susceptibility assessment, GIS, remote sensing techniques, and relevant factors contributing to landslides. This review will provide a theoretical framework for the study and identify gaps in current knowledge that the research intends to address. The methodology chapter will outline the approach taken to collect, process, and analyze data for the study. This will involve the acquisition of geospatial data such as topographic maps, satellite imagery, and geological information. GIS software will be used to integrate and analyze these datasets to identify landslide-prone areas based on factors such as slope gradient, geology, land cover, and rainfall patterns. Remote sensing techniques will complement GIS analysis by providing detailed information on land surface changes and vegetation cover that may indicate landslide susceptibility. The findings chapter will present the results of the analysis, including maps showing the spatial distribution of landslide susceptibility in the study area. The research will identify high-risk zones for landslides and assess the accuracy and reliability of the susceptibility model developed using GIS and remote sensing data. This chapter will also discuss the relationships between different factors and their influence on landslide occurrences in the specific region. In the conclusion and summary chapter, the research findings will be discussed in relation to the initial objectives and research questions. The significance of the study in terms of its contribution to landslide risk assessment and mitigation strategies will be highlighted. Recommendations for future research and practical implications for land use planning and disaster management will also be provided. Overall, this research aims to enhance the understanding of landslide susceptibility in a specific region by applying advanced geospatial technologies such as GIS and remote sensing. By identifying areas at high risk of landslides, the study seeks to contribute to proactive measures for disaster prevention and management, ultimately improving the resilience of communities and infrastructure in vulnerable regions.